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AI-Assisted Programming for Web and Machine Learning

AI-Assisted Programming for Web and Machine Learning

By : Christoffer Noring, Anjali Jain, Marina Fernandez, Ayşe Mutlu, Ajit Jaokar
4.9 (11)
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AI-Assisted Programming for Web and Machine Learning

AI-Assisted Programming for Web and Machine Learning

4.9 (11)
By: Christoffer Noring, Anjali Jain, Marina Fernandez, Ayşe Mutlu, Ajit Jaokar

Overview of this book

AI-Assisted Programming for Web and Machine Learning shows you how to build applications and machine learning models and automate repetitive tasks. Part 1 focuses on coding, from building a user interface to the backend. You’ll use prompts to create the appearance of an app using HTML, styling with CSS, adding behavior with JavaScript, and working with multiple viewports. Next, you’ll build a web API with Python and Flask and refactor the code to improve code readability. Part 1 ends with using GitHub Copilot to improve the maintainability and performance of existing code. Part 2 provides a prompting toolkit for data science from data checking (inspecting data and creating distribution graphs and correlation matrices) to building and optimizing a neural network. You’ll use different prompt strategies for data preprocessing, feature engineering, model selection, training, hyperparameter optimization, and model evaluation for various machine learning models and use cases. The book closes with chapters on advanced techniques on GitHub Copilot and software agents. There are tips on code generation, debugging, and troubleshooting code. You’ll see how simpler and AI-powered agents work and discover tool calling.
Table of Contents (25 chapters)
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3
Tools of the Trade: Introducing Our AI Assistants
23
Other Books You May Enjoy
24
Index

Step 4: Explore categorical variables

Just like before, let’s construct a prompt consisting of the output of the code we just ran and let Copilot tell us what should happen next:

[Prompt]

Below is the output:

Unnamed: 0 star_rating helpful_votes total_votes review_year count 100.000000 100.000000 100.000000 100.000000 100.0 mean 49.500000 0.840000 0.540000
0.960000 2015.0 std 29.011492 0.368453 1.298173 2.420118 0.0 min 0.000000 0.000000 0.000000 0.000000 2015.0 25% 24.750000 1.000000 0.000000 0.000000 2015.0 50% 49.500000 1.000000 0.000000 0.000000 2015.0 75% 74.250000 1.000000 1.000000 1.000000 2015.0 max 99.000000 1.000000 9.000000 14.000000 2015.0

[End of prompt]

You should receive a response similar to the response below:

[Prompt response]

Great! Now that we have the summary statistics, let’s move on to step 4: exploring categorical variables. We’ll analyze the unique values and their frequencies for categorical variables like...

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